The α-reliable path problem in stochastic road networks with link correlations: A moment-matching-based path finding algorithm

•A moment-matching-based hybrid genetic algorithm is proposed to search RSP.•Empirical travel time data from probe vehicles are utilized to measure TTR.•A moment-matching method is utilized to determine path TTD parameters.•Numerical studies based on a synthetic network and a real network are conduc...

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Veröffentlicht in:Expert systems with applications Jg. 110; S. 20 - 32
Hauptverfasser: Chen, Peng, Tong, Rui, Lu, Guangquan, Wang, Yunpeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 15.11.2018
Elsevier BV
Schlagworte:
Genetic algorithms
Goodness of fit
Hybrid genetic algorithm
Link correlations
Matching
Personalized routing guidance
Reliable shortest path
Roads & highways
Search algorithms
Search process
Shortest path algorithms
Shortest-path problems
Stochastic models
Stochastic road networks
Transportation networks
Travel time
Travel time reliability
Hybrid genetic algorithm
Reliable shortest path
Personalized routing guidance
Stochastic road networks
Link correlations
Travel time reliability
ISSN:0957-4174, 1873-6793
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Zusammenfassung:•A moment-matching-based hybrid genetic algorithm is proposed to search RSP.•Empirical travel time data from probe vehicles are utilized to measure TTR.•A moment-matching method is utilized to determine path TTD parameters.•Numerical studies based on a synthetic network and a real network are conducted. Most existing studies on routing guidance only paid attention to the average path travel time, which failed to consider travel time reliability (TTR) preferences by different travelers. In this study, a moment-matching-based hybrid genetic algorithm (MHGA) is proposed to search the reliable shortest path (RSP) in stochastic road networks with link correlations. First, the goodness-of-fit results based on field data reveal that lognormal distributions are more appropriate for characterizing link travel times. The impact of topological distance (measured by the number of links) and road type on link correlations is also scrutinized. Then, a moment-matching method (MOM) is utilized to determine the parameters of the approximate path travel time distribution (TTD) by accounting for link correlations. A local search algorithm is designed to improve the search ability of the path finding algorithm. In view of travelers’ risk tolerance, the algorithm enables the provision of personalized routing guidance for individual travelers. Furthermore, to support path finding applications in a large-scale network, heuristic constraints are imposed to help reduce the computational workload and accelerate the convergence speed of the search process. Finally, numerical case studies based on synthetic networks and a real road network in Beijing are presented, and the results help demonstrate that the algorithm has good potential to solve RSP searching problems in a large-scale network with desirable efficiency.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2018.05.022